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INF 2914 Information Retrieval and Web Search

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Title: INF 2914 Information Retrieval and Web Search


1
INF 2914Information Retrieval and Web Search
  • Lecture 1 Overview
  • These slides are adapted from Stanfords class
    CS276 / LING 286
  • Information Retrieval and Web Mining

2
Search use (iProspect Survey, 4/04,
http//www.iprospect.com/premiumPDFs/iProspectSurv
eyComplete.pdf)
3
Without search engines the web wouldnt scale
  • No incentive in creating content unless it can be
    easily found other finding methods havent kept
    pace (taxonomies, bookmarks, etc)
  • The web is both a technology artifact and a
    social environment
  • The Web has become the new normal in the
    American way of life those who dont go online
    constitute an ever-shrinking minority. Pew
    Foundation report, January 2005
  • Search engines make aggregation of interest
    possible
  • Create incentives for very specialized niche
    players
  • Economical specialized stores, providers, etc
  • Social narrow interests, specialized
    communities, etc
  • The acceptance of search interaction makes
    unlimited selection stores possible
  • Amazon, Netflix, etc
  • Search turned out to be the best mechanism for
    advertising on the web, a 15 B industry.
  • Growing very fast but entire US advertising
    industry 250B huge room to grow
  • Sponsored search marketing is about 10B

4
Classical IR vs. Web IR
5
Basic assumptions of Classical Information
Retrieval
  • Corpus Fixed document collection
  • Goal Retrieve documents with information content
    that is relevant to users information need

6
Classic IR Goal
  • Classic relevance
  • For each query Q and stored document D in a given
    corpus assume there exists relevance Score(Q, D)
  • Score is average over users U and contexts C
  • Optimize Score(Q, D) as opposed to Score(Q, D, U,
    C)
  • That is, usually
  • Context ignored
  • Individuals ignored
  • Corpus predetermined

7
Web IR
8
The coarse-level dynamics
Feeds
Crawls
Content creators
Content aggregators
Content consumers
9
Brief (non-technical) history
  • Early keyword-based engines
  • Altavista, Excite, Infoseek, Inktomi, ca.
    1995-1997
  • Paid placement ranking Goto.com (morphed into
    Overture.com ? Yahoo!)
  • Your search ranking depended on how much you paid
  • Auction for keywords casino was expensive!

10
Brief (non-technical) history
  • 1998 Link-based ranking pioneered by Google
  • Blew away all early engines save Inktomi
  • Great user experience in search of a business
    model
  • Meanwhile Goto/Overtures annual revenues were
    nearing 1 billion
  • Result Google added paid-placement ads to the
    side, independent of search results
  • Yahoo follows suit, acquiring Overture (for paid
    placement) and Inktomi (for search)

11
Ads
Algorithmic results.
12
Ads vs. search results
  • Google has maintained that ads (based on vendors
    bidding for keywords) do not affect vendors
    rankings in search results

Search miele
13
Ads vs. search results
  • Other vendors (Yahoo, MSN) have made similar
    statements from time to time
  • Any of them can change anytime
  • We will focus primarily on search results
    independent of paid placement ads
  • Although the latter is a fascinating technical
    subject in itself

14
Web search basics
15
User Needs
  • Need Brod02, RL04
  • Informational want to learn about something
    (40 / 65)
  • Navigational want to go to that page (25 /
    15)
  • Transactional want to do something
    (web-mediated) (35 / 20)
  • Access a service
  • Downloads
  • Shop
  • Gray areas
  • Find a good hub
  • Exploratory search see whats there

Low hemoglobin
United Airlines
Car rental Brasil
16
Web search users
  • Make ill defined queries
  • Short
  • AV 2001 2.54 terms avg, 80 lt 3 words)
  • AV 1998 2.35 terms avg, 88 lt 3 words Silv98
  • Imprecise terms
  • Sub-optimal syntax (most queries without
    operator)
  • Low effort
  • Wide variance in
  • Needs
  • Expectations
  • Knowledge
  • Bandwidth
  • Specific behavior
  • 85 look over one result screen only (mostly
    above the fold)
  • 78 of queries are not modified (one
    query/session)

17
Query Distribution
Power law few popular broad queries,
many rare specific queries
18
How far do people look for results?
(Source iprospect.com WhitePaper_2006_SearchEngin
eUserBehavior.pdf)
19
Users empirical evaluation of results
  • Quality of pages varies widely
  • Relevance is not enough
  • Other desirable qualities (non IR!!)
  • Content Trustworthy, new info, non-duplicates,
    well maintained,
  • Web readability display correctly fast
  • No annoyances pop-ups, etc
  • Precision vs. recall
  • On the web, recall seldom matters
  • What matters
  • Precision at 1? Precision above the fold?
  • Comprehensiveness must be able to deal with
    obscure queries
  • Recall matters when the number of matches is very
    small
  • User perceptions may be unscientific, but are
    significant over a large aggregate

20
Users empirical evaluation of engines
  • Relevance and validity of results
  • UI Simple, no clutter, error tolerant
  • Trust Results are objective
  • Coverage of topics for poly-semic queries
  • Pre/Post process tools provided
  • Mitigate user errors (auto spell check, syntax
    errors,)
  • Explicit Search within results, more like this,
    refine ...
  • Anticipative related searches
  • Deal with idiosyncrasies
  • Web specific vocabulary
  • Impact on stemming, spell-check, etc
  • Web addresses typed in the search box

21
Loyalty to a given search engine(iProspect
Survey, 4/04)
22
The Web corpus
  • No design/co-ordination
  • Distributed content creation, linking,
    democratization of publishing
  • Content includes truth, lies, obsolete
    information, contradictions
  • Unstructured (text, html, ), semi-structured
    (XML, annotated photos), structured (Databases)
  • Scale much larger than previous text corpora
    but corporate records are catching up.
  • Growth slowed down from initial volume
    doubling every few months but still expanding
  • Content can be dynamically generated

23
The Web Dynamic content
  • A page without a static html version
  • E.g., current status of flight AA129
  • Current availability of rooms at a hotel
  • Usually, assembled at the time of a request from
    a browser
  • Typically, URL has a ? character in it

Application server
24
Dynamic content
  • Most dynamic content is ignored by web spiders
  • Many reasons including malicious spider traps
  • Some dynamic content (news stories from
    subscriptions) are sometimes delivered as dynamic
    content
  • Application-specific spidering
  • Spiders commonly view web pages just as Lynx (a
    text browser) would
  • Note even static pages are typically assembled
    on the fly (e.g., headers are common)

25
The web size
  • What is being measured?
  • Number of hosts
  • Number of (static) html pages
  • Volume of data
  • Number of hosts netcraft survey
  • http//news.netcraft.com/archives/web_server_surve
    y.html
  • Monthly report on how many web hosts servers
    are out there
  • Number of pages numerous estimates (will
    discuss later)

26
Netcraft Web Server Surveyhttp//news.netcraft.co
m/archives/web_server_survey.html
27
The web evolution
  • All of these numbers keep changing
  • Relatively few scientific studies of the
    evolution of the web Fetterly al, 2003
  • http//research.microsoft.com/research/sv/sv-pubs/
    p97-fetterly/p97-fetterly.pdf
  • Sometimes possible to extrapolate from small
    samples (fractal models) Dill al, 2001
  • http//www.vldb.org/conf/2001/P069.pdf

28
Rate of change
  • Cho00 720K pages from 270 popular sites sampled
    daily from Feb 17 Jun 14, 1999
  • Any changes 40 weekly, 23 daily
  • Fett02 Massive study 151M pages checked over
    few months
  • Significant changed -- 7 weekly
  • Small changes 25 weekly
  • Ntul04 154 large sites re-crawled from scratch
    weekly
  • 8 new pages/week
  • 8 die
  • 5 new content
  • 25 new links/week

29
Static pages rate of change
  • Fetterly et al. study (2002) several views of
    data, 150 million pages over 11 weekly crawls
  • Bucketed into 85 groups by extent of change

30
Other characteristics
  • Significant duplication
  • Syntactic 30-40 (near) duplicates Brod97,
    Shiv99b, etc.
  • Semantic ???
  • High linkage
  • More than 8 links/page in the average
  • Complex graph topology
  • Not a small world bow-tie structure Brod00
  • Spam
  • Billions of pages

31
Answering the need behind the query
  • Semantic analysis
  • Query language determination
  • Auto filtering
  • Different ranking (if query in Japanese do not
    return English)
  • Hard soft (partial) matches
  • Personalities (triggered on names)
  • Cities (travel info, maps)
  • Medical info (triggered on names and/or results)
  • Stock quotes, news (triggered on stock symbol)
  • Company info
  • Etc.
  • Natural Language reformulation
  • Integration of Search and Text Analysis

32
Yahoo! britney spears
33
Ask Jeeves las vegas
34
Yahoo! salvador hotels
35
Yahoo shortcuts
  • Various types of queries that are understood

36
Google andrei broder new york
37
Answering the need behind the query Context
  • Context determination
  • spatial (user location/target location)
  • query stream (previous queries)
  • personal (user profile)
  • explicit (user choice of a vertical search, )
  • implicit (use Google from France, use google.fr)
  • Context use
  • Result restriction
  • Kill inappropriate results
  • Ranking modulation
  • Use a rough generic ranking, but personalize
    later

38
Google dentists bronx
39
Yahoo! dentists (bronx)
40
(No Transcript)
41
Query expansion
42
Web Search Components
  • Crawler
  • Stores raw documents along with per-document and
    per-server metadata in a database
  • Parser/tokenizer
  • Processes the raw documents to generate a
    tokenized documents
  • Handles different files types (HTML, PDF, etc)
  • Store
  • Storage for the tokenized version of each
    document

43
Web Search Components
  • Index
  • Inverted text index over the Store
  • Global analysis
  • Duplicate detection, ranks, and anchor text
    processing
  • Runtime
  • Query processing
  • Ranking (dynamic)

44
(Offline) Search Engine Data Flow
Parse Tokenize
Global Analysis
Index Build
Crawler
- Scan tokenized web pages, anchor text,
etc- Generate text index
web page
- Parse- Tokenize- Per page analysis
- Dup detection- Static rank comp- Anchor
text -
2
1
3
4
in background
duptable
tokenizedweb pages
ranktable
anchortext
invertedtext index
45
Class Schedule
  • Lecture 1 Overview
  • Lecture 2 Crawler
  • Lecture 3 Parsing, Tokenization, Storage
  • Lecture 4 Link Analysis
  • Static ranking, anchor text
  • Lecture 5 Other Global Analysis
  • Duplicate detection, Web spam
  • Lectures 6 7 Indexing
  • Lectures 8 9 Query Processing Ranking
  • Lecture 10 Evaluation (IR Metrics)
  • Lectures 11-15 Student projects
  • Potential extra lectures Advertizing/XML
    Retrieval, Machine Learning, Compression

46
Projects
  • Each class has a list of papers that students can
    select for a written paper, implementation, and
    lecture
  • Students have to discuss the implementation
    projects with the teachers
  • Students have until May 3rd to select a project
    topic

47
Resources
  • http//www-di.inf.puc-rio.br/laber/MaquinaBusca20
    07-1.htm
  • IIR Chapter 19

48
Project 1 - Web measurements
  • References
  • Sampling
  • Ziv Bar-Yossef, Maxim Gurevich Random sampling
    from a search engine's index. WWW 2006 367-376
  • Index size
  • Andrei Z. Broder et. al. Estimating corpus size
    via queries. CIKM 2006 594-603
  • Brazilian Web
  • http//homepages.dcc.ufmg.br/nivio/papers/semish0
    5.pdf
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